R Programming Statistical Data Analysis in Research /
This book is written for statisticians, data analysts, programmers, researchers, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using R object-oriented programming language and RStudio integrated development environment (IDE)...
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| Autors principals: | , |
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| Autor corporatiu: | |
| Format: | Electrònic eBook |
| Idioma: | anglès |
| Publicat: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2024.
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| Edició: | 1st ed. 2024. |
| Matèries: | |
| Accés en línia: | https://doi.org/10.1007/978-981-97-3385-9 |
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Taula de continguts:
- Introduction to R programming and RStudio Integrated Development Environment (IDE)
- Working with Data in R: Objects, Vectors, Factors, Packages and Libraries, and Data Visualization
- Test of Normality and Reliability of Data in R
- Choosing between Parametric and Non-Parametric Tests in Statistical Data Analysis
- Understanding Dependent and Independent Variables in Research Experiments and Hypothesis Testing
- Understanding the Different Types of Statistical Data Analysis and Methods
- Regression Analysis in R: Linear and Logistic Regression
- T-test Statistics in R: Independent samples, Paired sample, and One sample ttests
- Analysis of Variance (ANOVA) in R: One-way and Two-way ANOVA
- Chi-squared (X2) Statistical Test in R
- Mann Whitney U test and Kruskal Wallis H test Statistics in R
- Correlation Tests in R: Pearson cor, Kendall’s tau, and Spearman’s rho
- Wilcoxon Statistics in R: Signed-Rank test and Rank-Sum test.